Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence

Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a "picture" not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining "vague" when there is not enough "evidence" in the data or standard modeling constructs do not "fit". Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.Comment: 25 pages, 12 figure

Process discovery using in-database minimum self distance abstractions

\u3cp\u3eProcess executions generate event data that are typically stored in legacy information systems, such as databases. However, process discovery, which requires such event data, is performed in main memory. To bridge this gap, existing techniques must transform and extract event data, which can be expensive steps. This issue has been addressed by processing the event data directly in their origin. However, existing methods rely only on the simplest event data abstraction: the Directly Follows (DF) abstraction. This paper improves upon these existing works by considering another abstraction, the Minimum Self Distance (MSD) abstraction, which enables discovery of a larger class of models than the DF alone. That is, we propose IMw, a process discovery technique without logs and uses both the MSD and DF abstractions. Furthermore, this work proposes an approach to compute the MSD abstraction in-database, thus avoiding the need for transforming and moving event data. We evaluate IMw with real-life logs, and the experimental results show that IMw with in-database abstraction is faster than the traditional approach, aware of dynamic updates on event data, and able to discover models with pareto-optimal results, compared to existing techniques.\u3c/p\u3

Information-preserving abstractions of event data in process mining

\u3cp\u3eProcess mining aims at obtaining information about processes by analysing their past executions in event logs, event streams, or databases. Discovering a process model from a finite amount of event data thereby has to correctly infer infinitely many unseen behaviours. Thereby, many process discovery techniques leverage abstractions on the finite event data to infer and preserve behavioural information of the underlying process. However, the fundamental information-preserving properties of these abstractions are not well understood yet. In this paper, we study the information-preserving properties of the “directly follows” abstraction and its limitations. We overcome these by proposing and studying two new abstractions which preserve even more information in the form of finite graphs. We then show how and characterize when process behaviour can be unambiguously recovered through characteristic footprints in these abstractions. Our characterization defines large classes of practically relevant processes covering various complex process patterns. We prove that the information and the footprints preserved in the abstractions suffice to unambiguously rediscover the exact process model from a finite event log. Furthermore, we show that all three abstractions are relevant in practice to infer process models from event logs and outline the implications on process mining techniques.\u3c/p\u3

Learning Hybrid Process Models from Events

none4siProcess discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a “picture” not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining “vague” when there is not enough “evidence” in the data or standard modeling constructs do not “fit”. Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.van der Aalst, Wil M. P.; De Masellis, Riccardo; Di Francescomarino, Chiara; Ghidini, Chiaravan der Aalst, Wil M. P.; De Masellis, Riccardo; Di Francescomarino, Chiara; Ghidini, Chiar

Discovering queues from event logs with varying levels of information

\u3cp\u3eDetecting and measuring resource queues is central to business process optimization. Queue mining techniques allow for the identification of bottlenecks and other process inefficiencies, based on event data. This work focuses on the discovery of resource queues. In particular, we investigate the impact of available information in an event log on the ability to accurately discover queue lengths, i.e. the number of cases waiting for an activity. Full queueing information, i.e. timestamps of enqueueing and exiting the queue, makes queue discovery trivial. However, often we see only the completions of activities. Therefore, we focus our analysis on logs with partial information, such as missing enqueueing times or missing both enqueueing and service start times. The proposed discovery algorithms handle concurrency and make use of statistical methods for discovering queues under this uncertainty. We evaluate the techniques using real-life event logs. A thorough analysis of the empirical results provides insights into the influence of information levels in the log on the accuracy of the measurements.\u3c/p\u3

Fast incremental conformance analysis for interactive process discovery

\u3cp\u3eInteractive process discovery allows users to specify domain knowledge while discovering process models with the help of event logs. Typically the coherence of an event log and a process model is calculated using conformance analysis. Many state-of-the-art conformance techniques emphasize on the correctness of the results, and hence can be slow, impractical and undesirable in interactive process discovery setting, especially when the process models are complex. In this paper, we present a framework (and its application) to calculate conformance fast enough to guide the user in interactive process discovery. The proposed framework exploits the underlying techniques used for interactive process discovery in order to incrementally update the conformance results. We trade the accuracy of conformance for performance. However, the user is also provided with some diagnostic information, which can be useful for decision making in an interactive process discovery setting. The results show that our approach can be considerably faster than the traditional approaches and hence better suited in an interactive setting.\u3c/p\u3

A Tour in Process Mining : From Practice to Algorithmic Challenges

Process mining seeks the confrontation between modeled behavior and observed behavior. In recent years, process mining techniques managed to bridge the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining is used by many data-driven organizations as a means to improve performance or to ensure compliance. Traditionally, the focus was on the discovery of process models from event logs describing real process executions. However, process mining is not limited to process discovery and also includes conformance checking. Process models (discovered or hand-made) may deviate from reality. Therefore, we need powerful means to analyze discrepancies between models and logs. These are provided by conformance checking techniques that first align modeled and observed behavior, and then compare both. The resulting alignments are also used to enrich process models with performance related information extracted from the event log. This tutorial paper focuses on the control-flow perspective and describes a range of process discovery and conformance checking techniques. The goal of the paper is to show the algorithmic challenges in process mining. We will show that process mining provides a wealth of opportunities for people doing research on Petri nets and related models of concurrency.Peer Reviewe